Face beauty prediction method based on multi-task learning

Abstract

The invention provides a human face beauty prediction method based on multi-task learning. The method comprises the steps of constructing a multi-task human face database and constructing a multi-taskhuman face beauty prediction model. According to the invention, the accuracy of face beauty prediction is enhanced by adding expression recognition and age recognition. In the multi-task face database construction process, the constructed database image comprises three labels including a face expression attribute, an age attribute and a face beauty degree attribute, so that subsequent multi-tasktraining and prediction are facilitated. Network parameters are shared among tasks in a multi-task training process. Shared features are learned, so that the accuracy of learning a single task by a network is improved. The multi-task learning is carried out by using a deep learning network. A shared representation layer can enable tasks with generality to be better combined with correlation information. A task specific layer can independently model task specific information. Network parameters can be optimized by using samples from different tasks. Meanwhile, the multi-task performance is improved.

Description

technical field [0001] The invention relates to the technical field of human face beauty evaluation using image processing and machine learning technology, in particular to a multi-task learning-based human face beauty prediction method. Background technique [0002] It is human nature to love beauty, and everyone has the heart to love beauty. Aristotle said: "A beautiful face is a better testimonial". The favorable impression of beauty exists in daily life and has a great influence on people's daily life. Research on the beauty of human face is a frontier topic in the research on the nature and laws of human cognition that has emerged in recent years. Exploring how to better measure beauty will help to obtain scientific, objective and quantifiable human face beauty codes, an eternal theme of human beings. The description of human face beauty has made great progress in the interdisciplinary field of human face beauty research. [0003] In real life, people have different ...

AI Technical Summary

Problems solved by technology

However, the use of deep learning methods for face beauty research requires a large number of training samples, and the existing databases for face beauty prediction research are generally small, so it is not only difficult to directly train a deep network model, but also prone to overfitting problems. The existing face beauty evaluation can only be predicted based on a single task, but the evaluation of face beauty is affected by many factors, resulting in inaccurate face beauty evaluation and little reference value

Images